On Measures of Biases and Harms in NLP

Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang


Abstract
Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. To create interventions and mitigate these biases and associated harms, it is vital to be able to detect and measure such biases. While existing works propose bias evaluation and mitigation methods for various tasks, there remains a need to cohesively understand the biases and the specific harms they measure, and how different measures compare with each other. To address this gap, this work presents a practical framework of harms and a series of questions that practitioners can answer to guide the development of bias measures. As a validation of our framework and documentation questions, we also present several case studies of how existing bias measures in NLP—both intrinsic measures of bias in representations and extrinsic measures of bias of downstream applications—can be aligned with different harms and how our proposed documentation questions facilitates more holistic understanding of what bias measures are measuring.
Anthology ID:
2022.findings-aacl.24
Volume:
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
Month:
November
Year:
2022
Address:
Online only
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
246–267
Language:
URL:
https://aclanthology.org/2022.findings-aacl.24
DOI:
Bibkey:
Cite (ACL):
Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, and Kai-Wei Chang. 2022. On Measures of Biases and Harms in NLP. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 246–267, Online only. Association for Computational Linguistics.
Cite (Informal):
On Measures of Biases and Harms in NLP (Dev et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-aacl.24.pdf